# ifndef CPPAD_EXAMPLE_ABS_NORMAL_LP_BOX_HPP
# define CPPAD_EXAMPLE_ABS_NORMAL_LP_BOX_HPP
/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-17 Bradley M. Bell

CppAD is distributed under multiple licenses. This distribution is under
the terms of the
                    Eclipse Public License Version 1.0.

A copy of this license is included in the COPYING file of this distribution.
Please visit http://www.coin-or.org/CppAD/ for information on other licenses.
-------------------------------------------------------------------------- */
/*
$begin lp_box$$
$spell
	hpp
	lp
	const
	col
	xout
	yout
	sout
	cols
	prog
	maxitr
	xin
	qp
$$
$section abs_normal: Solve a Linear Program With Box Constraints$$

$head Syntax$$
$icode%ok% = lp_box(
	%level%, %A%, %b%, %c%, %d%, %maxitr%, %xout%
)%$$

$head Prototype$$
$srcfile%example/abs_normal/lp_box.hpp%
	0%// BEGIN PROTOTYPE%// END PROTOTYPE%
1%$$

$head Source$$
This following is a link to the source code for this example:
$cref/lp_box.hpp/lp_box.hpp/$$.

$head Problem$$
We are given
$latex A \in \B{R}^{m \times n}$$,
$latex b \in \B{R}^m$$,
$latex c \in \B{R}^n$$,
$latex d \in \B{R}^n$$,
This routine solves the problem
$latex \[
\begin{array}{rl}
\R{minimize} &
c^T x \; \R{w.r.t} \; x \in \B{R}^n
\\
\R{subject \; to} & A x + b \leq 0 \; \R{and} \; - d \leq x \leq d
\end{array}
\] $$

$head Vector$$
The type $icode Vector$$ is a
simple vector with elements of type $code double$$.

$head level$$
This value is less that or equal two.
If $icode%level% == 0%$$,
no tracing is printed.
If $icode%level% >= 1%$$,
a trace of the $code lp_box$$ operations is printed.
If $icode%level% >= 2%$$,
the objective and primal variables $latex x$$ are printed
at each $cref simplex_method$$ iteration.
If $icode%level% == 3%$$,
the simplex tableau is printed at each simplex iteration.

$head A$$
This is a $cref/row-major/glossary/Row-major Representation/$$ representation
of the matrix $latex A$$ in the problem.

$head b$$
This is the vector $latex b$$ in the problem.

$head c$$
This is the vector $latex c$$ in the problem.

$head d$$
This is the vector $latex d$$ in the problem.
If $latex d_j$$ is infinity, there is no limit for the size of
$latex x_j$$.

$head maxitr$$
This is the maximum number of newton iterations to try before giving up
on convergence.

$head xout$$
This argument has size is $icode n$$ and
the input value of its elements does no matter.
Upon return it is the primal variables
$latex x$$ corresponding to the problem solution.

$head ok$$
If the return value $icode ok$$ is true, an optimal solution was found.

$children%example/abs_normal/lp_box.cpp
	%example/abs_normal/lp_box.omh
%$$
$head Example$$
The file $cref lp_box.cpp$$ contains an example and test of
$code lp_box$$.
It returns true if the test passes and false otherwise.

$end
-----------------------------------------------------------------------------
*/
# include "simplex_method.hpp"

// BEGIN C++
namespace CppAD { // BEGIN_CPPAD_NAMESPACE

// BEGIN PROTOTYPE
template <class Vector>
bool lp_box(
	size_t        level   ,
	const Vector& A       ,
	const Vector& b       ,
	const Vector& c       ,
	const Vector& d       ,
	size_t        maxitr  ,
	Vector&       xout    )
// END PROTOTYPE
{	double inf = std::numeric_limits<double>::infinity();
	//
	size_t m = b.size();
	size_t n = c.size();
	//
	CPPAD_ASSERT_KNOWN(
		level <= 3, "lp_box: level is greater than 3");
	CPPAD_ASSERT_KNOWN(
		size_t(A.size()) == m * n, "lp_box: size of A is not m * n"
	);
	CPPAD_ASSERT_KNOWN(
		size_t(d.size()) == n, "lp_box: size of d is not n"
	);
	if( level > 0 )
	{	std::cout << "start lp_box\n";
		CppAD::abs_print_mat("A", m, n, A);
		CppAD::abs_print_mat("b", m, 1, b);
		CppAD::abs_print_mat("c", n, 1, c);
		CppAD::abs_print_mat("d", n, 1, d);
	}
	//
	// count number of limits
	size_t n_limit = 0;
	for(size_t j = 0; j < n; j++)
	{	if( d[j] < inf )
			n_limit += 1;
	}
	//
	// A_simplex and b_simplex define the extended constraints
	Vector A_simplex((m + 2 * n_limit) * (2 * n) ), b_simplex(m + 2 * n_limit);
	for(size_t i = 0; i < size_t(A_simplex.size()); i++)
		A_simplex[i] = 0.0;
	//
	// put A * x + b <= 0 in A_simplex, b_simplex
	for(size_t i = 0; i < m; i++)
	{	b_simplex[i] = b[i];
		for(size_t j = 0; j < n; j++)
		{	// x_j^+ coefficient (positive component)
			A_simplex[i * (2 * n) + 2 * j]     =   A[i * n + j];
			// x_j^- coefficient (negative component)
			A_simplex[i * (2 * n) + 2 * j + 1] = - A[i * n + j];
		}
	}
	//
	// put | x_j | <= d_j in A_simplex, b_simplex
	size_t i_limit = 0;
	for(size_t j = 0; j < n; j++) if( d[j] < inf )
	{
		// x_j^+ <= d_j constraint
		b_simplex[ m + 2 * i_limit]                         = - d[j];
		A_simplex[(m + 2 * i_limit) * (2 * n) + 2 * j]      = 1.0;
		//
		// x_j^- <= d_j constraint
		b_simplex[ m + 2 * i_limit + 1]                         = - d[j];
		A_simplex[(m + 2 * i_limit + 1) * (2 * n) + 2 * j + 1]  = 1.0;
		//
		++i_limit;
	}
	//
	// c_simples
	Vector c_simplex(2 * n);
	for(size_t j = 0; j < n; j++)
	{	// x_j+ component
		c_simplex[2 * j]     = c[j];
		// x_j^- component
		c_simplex[2 * j + 1] = - c[j];
	}
	size_t level_simplex = 0;
	if( level >= 2 )
		level_simplex = level - 1;
	//
	Vector x_simplex(2 * n);
	bool ok = CppAD::simplex_method(
		level_simplex, A_simplex, b_simplex, c_simplex, maxitr, x_simplex
	);
	for(size_t j = 0; j < n; j++)
		xout[j] = x_simplex[2 * j] - x_simplex[2 * j + 1];
	if( level > 0 )
	{	CppAD::abs_print_mat("xout", n, 1, xout);
		if( ok )
			std::cout << "end lp_box: ok = true\n";
		else
			std::cout << "end lp_box: ok = false\n";
	}
	return ok;
}

} // END_CPPAD_NAMESPACE
// END C++

# endif
